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Fantahun M. DefershaView profile

Professor

• Manufacturing system analysis
• Flexible and cellular manufacturing systems
• Reconfigurable manufacturing systems
• +7 more

Fantahun M. Defersha is a Full Professor in the Department of Mechanical and Industrial Engineering at the University of Guelph, Ontario, Canada. He holds a PhD in Mechanical Engineering from Concordia University (2006) and has over 28 years of academic experience, including roles as Area Head in Mechanical Engineering. His research focuses on manufacturing systems optimization, cellular manufacturing, supply chain modeling, meta-heuristics, and parallel computing applications. Education: B.Sc. Mechanical Engineering (1995), Addis Ababa University MEng. Mechanical Engineering (2000), University of Roorkee (IIT Roorkee) PhD Mechanical Engineering (2006), Concordia University Research Interests: Manufacturing system analysis, flexible/cellular manufacturing systems, reconfigurable manufacturing systems, supply chain optimization, meta-heuristics, parallel computing, and additive manufacturing sustainability. His work integrates computational methods like genetic algorithms and machine learning to solve complex industrial problems. Publications: Over 50 peer-reviewed journal articles, emphasizing sustainable manufacturing, optimization algorithms, and industrial systems. Recent work includes hybrid machine learning approaches for additive manufacturing and cloud-based digital twin systems. Honors: Campaign for a New Millennium Graduate Scholarship (2004–2005) Concordia University International Tuition Fees Remission Award (2004–2005) Concordia University Graduate Fellowship (2004–2005) Teaching & Advising: Taught over 25 courses, including Optimization in Engineering, Discrete Event Simulation, and Manufacturing Systems Design. Actively advises graduate students in mechanical and industrial engineering. Current research funding includes NSERC grants for Industry 4.0 integration and digital twin technologies. Labs/Teams: Leads research on smart manufacturing systems, digital twins, and sustainable production processes through collaborations with industry partners like AVL Manufacturing Inc.

William N. SumnerView profile

Associate Professor

• Automated Debugging
• Concurrency & Parallelism
• Program Analysis & Transformation
• +3 more

William N. Sumner is an Associate Professor and Associate Director, Academic and Administration in the School of Computing Science at Simon Fraser University. He holds a Ph.D. in Computer Science from Purdue University (2013) and a B.Sc. in Computer Science from Hope College (2005). His research focuses on automated debugging tools, concurrency, parallelism, program analysis, and software quality. He teaches courses such as Software Development Methods, Software Testing, and Automated Software Analysis. Sumner actively recruits graduate and undergraduate students for research in improving software development processes through automation. His work emphasizes reducing repetitive tasks in software development using techniques like test case reduction and program analysis. Education: Ph.D., Computer Science, Purdue University, 2013 B.Sc., Computer Science, Hope College, 2005 Research Interests: Sumner’s research addresses challenges in software development automation, including automated debugging, test suite assessment, and program hardening. He develops tools like Pardis to optimize test case reduction and improve software reliability. His work intersects with concurrency, parallelism, and program transformation to enhance developer productivity and software quality. Recent Contributions: Recent publications include advancements in type batched program reduction (2023) and leveraging models for test case reduction in software repositories (2021). His Pardis framework (2019) improves test case reduction efficiency by addressing priority inversion in structured inputs. Teaching & Engagement: Sumner teaches courses such as CMPT 373 (Software Development Methods), CMPT 473 (Software Testing), and CMPT 479 (Automated Software Analysis). He emphasizes hands-on projects, such as the Geotagged Meeting Planner in CMPT 276, focusing on teamwork, agile methodologies, and software engineering best practices.

Pooya Moradian ZadehView profile

Professor

• Artificial Intelligence
• Public Health
• Social Network Analysis
• +4 more

Prof. Pooya Moradian Zadeh is a Professor in the School of Computer Science at the University of Windsor, specializing in Artificial Intelligence and its applications in public health, social networks, and pandemic response. His research focuses on leveraging AI to address challenges like social isolation, disease surveillance, and healthcare accessibility. He has led interdisciplinary projects such as the $15 million INSPIRE initiative for pandemic preparedness and contributed to the WE-Spark Health Institute's COVID-19 Dashboard . Recognized for his teaching excellence, he received the 2024 Alumni Association Distinguished Teaching Award . His work involves collaborations with institutions like the Great Lakes Institute for Environmental Research and WE-Spark, addressing topics ranging from recommender systems to compassionate community approaches for vulnerable populations. He mentors students like Saghi Khani and Kameswara Peddada, guiding projects such as Simplified SCO on Wheels , which improves retail checkout processes. Research & Grants : Prof. Zadeh has secured grants totaling over $15M for pandemic response, AI-driven health solutions, and social network analysis. His teams develop algorithms for social isolation detection, personalized healthcare recommendations, and agent-based models for public health policy evaluation. He is also involved in advancing conversational AI for healthcare accessibility and optimizing team formation in digital networks. Labs & Collaborations : He leads projects at the University of Windsor’s 300 Ouellette Ave. campus and collaborates with industry and academic partners to bridge AI innovation with real-world health challenges. His work emphasizes ethical AI practices and community-focused solutions.

Prasad DaggupatiView profile

Associate Professor

• Water Resources Engineering
• Environmental Engineering
• Agricultural Management
• +5 more

Prasad Daggupati is an Associate Professor in the School of Engineering at the University of Guelph, specializing in Water Resources Engineering. He holds a Ph.D. from Kansas State University and has held academic positions since 2016, including Assistant Professor and Associate Professor roles. His research focuses on watershed processes, climate change impacts, and the application of remote sensing technologies. Key areas include ephemeral gully erosion, critical source area identification, and hydrological modeling. He has been recognized with awards such as the 2022 Young Engineer Award (NABEC) and the 2021 Outstanding Associate Editor Recognition (ASABE Journal). Education includes a Ph.D. (2012) and MS (2012) in Biological and Agricultural Engineering from Kansas State University, and a BS (2004) from Acharya N.G. Ranga Agricultural University. His teaching spans undergraduate courses like ENGG 4360 (Soil-Water Conservation Systems Design) and graduate courses such as ENGG 6800 (Deterministic Hydrological Modeling). Research interests emphasize agricultural management's impact on sediment and nutrient transport, climate change adaptation, and precision agriculture. He leads a team of over 30 graduate students and has collaborated on projects funded by agencies like USDA-NIFA and NRCS. His work integrates machine learning, geospatial tools, and hydrological models to address water security and environmental sustainability challenges. Professional contributions include over 100 peer-reviewed publications, with a focus on SWAT modeling, non-point source pollution, and UAV applications. His lab develops decision frameworks for sustainable watershed management, particularly in Ontario's agricultural regions.

Peter WestView profile

Assistant Professor

• Natural Language Processing
• Artificial Intelligence
• Large Language Models
• +3 more

Peter West is an incoming Assistant Professor at the University of British Columbia (UBC) Computer Science Department, specializing in Natural Language Processing (NLP) and AI. His research focuses on understanding the capabilities and limitations of large language models (LLMs) and generative AI systems, emphasizing their divergence from human intuition and alignment challenges. He holds a PhD from the University of Washington (2024), supervised by Yejin Choi, and a BSc (Honours Computer Science) from UBC (2017). His work has been recognized with awards including Best Method Paper at NAACL 2022 and Outstanding Paper awards at ACL 2023 and EMNLP 2023. He conducted internships at the Allen Institute for AI and Microsoft Research’s NLP group. Research interests include analyzing LLM behavior through a natural sciences lens, exploring model capabilities versus human expectations, and developing decoding algorithms to infuse models with algorithmic logic. His recent publications address generative AI paradoxes, constrained text generation, and symbolic knowledge distillation. He serves on panels for NeurIPS workshops and is beginning a postdoc at Stanford with Chris Potts. His research group at UBC seeks students interested in generative AI’s analytical frontiers.

• Computer Science
• Algorithm Design
• Parallel Computing
• +4 more

Susanne Bradley is a Teaching Professor in the Department of Computer Science at the University of British Columbia (UBC). She specializes in algorithm design, parallel computation, and numerical analysis. Her teaching responsibilities include courses such as CPSC 320 (Intermediate Algorithm Design and Analysis) and CPSC 418 (Parallel Computation). Her research focuses on preconditioners for saddle-point systems, eigenvalue analysis, and innovative educational methodologies like inverted two-stage exams. Bradley’s academic contributions span numerical linear algebra, computational methods, and biomechanical simulation. She has published extensively on topics such as eigenvalue bounds for saddle-point systems and the application of machine learning in sensorimotor control. Her work emphasizes practical solutions for complex computational challenges and pedagogical innovations to enhance learning outcomes. Her office is located in ICCS 241, and she can be reached at smbrad@cs.ubc.ca .

Ahmad MohammadpanahView profile

Lecturer

• Dynamics
• Vibrations
• Modal Analysis
• +5 more

Ahmad Mohammadpanah serves as a Lecturer in the Department of Mechanical Engineering within the Faculty of Applied Science at the University of British Columbia (UBC). Holding a Professional Engineer (P.Eng.) designation, he earned his PhD and MASc from UBC alongside an MSc from Sharif University of Technology. His academic profile is anchored in mechanical engineering with specialized expertise in dynamic systems and manufacturing technologies. His educational qualifications include: PhD in Mechanical Engineering, University of British Columbia MASc in Mechanical Engineering, University of British Columbia MSc in Mechanical Engineering, Sharif University of Technology Dr. Mohammadpanah's research centers on Dynamics, Vibrations, and Modal Analysis , with pioneering work in Spherical Parallel Robots and Light Weight Lattice Design for Additive Manufacturing . His exploration of Generative Design & Topology Optimization bridges computational methods with practical manufacturing solutions. Current investigations focus on thermal management in metal extrusion additive manufacturing and vibration control in industrial saw systems, demonstrating strong alignment with industry needs in wood processing and advanced manufacturing. Analysis of his 10 publications (2012-2024) reveals consistent focus on vibration phenomena in rotating machinery and additive manufacturing defect mitigation. His work shows progressive evolution from fundamental dynamics research (2012-2018) toward applied industrial solutions (2021-2024), particularly in metal additive manufacturing and smart lumber production. The integration of machine learning in wood quality prediction (2023) highlights his adaptation to emerging computational techniques. No scientific awards were documented in the provided materials. Regarding academic mentorship, Dr. Mohammadpanah actively supervises undergraduate capstone projects (MECH 45X) and laboratory courses, though formal graduate student advisement isn't specified. His teaching portfolio spans foundational courses like MECH 223 (Introduction to Mechanical Design) through advanced topics including MANU 465 (AI in Manufacturing), reflecting comprehensive engagement with mechanical engineering education. His research operates through the "Intelligent Engineering" initiative, focusing on practical industrial applications rather than dedicated laboratory facilities. Current work emphasizes real-world implementation of vibration control systems and additive manufacturing optimization, with strong industry connections evident through Fpinnovations collaboration and patent development.

Nick BrodyView profile

Associate Professor

• Social Implications of Technology
• Bystander Behavior in Cyberbullying
• Relational Communication
• +1 more

Nick Brody is an Associate Professor of Communication Studies at the University of Puget Sound. His research focuses on the social implications of technology, particularly how digital artifacts affect emotional recovery after romantic breakups. He teaches courses in communication theory, quantitative research methods, communication and technology, and relational communication. 2019: Thomas A. Davis Teaching Award 2016: Puget Sound Faculty Research Award Research Interests Brody's work explores: Bystander behavior in cyberbullying episodes Communication patterns in on-again/off-again relationships Digital memory persistence and its psychological impact The cognitive parallels between social and physical pain Recent Article Trends His recent publications analyze how social media algorithms and network connections complicate post-breakup recovery, emphasizing the need for intentional digital curation. The Virtual Relational Memory model (2022) highlights the interplay of digital objects, narrative control, and social networks. Scientific Contributions Developed the Virtual Relational Memory framework Published in Communication Monographs , Computers in Human Behavior , and other top-tier journals Identified correlations between nostalgic tendencies and post-breakup adjustment

Serpil KocabiyikView profile

Professor

• Fluid Mechanics
• Computational Science
• Theoretical Fluid Dynamics
• +3 more

Serpil Kocabiyik is a Professor of Mathematics at Memorial University of Newfoundland, leading research in fluid mechanics and computational science. She holds a Ph.D. from Western Ontario (1987) and has held academic positions at Manitoba and Western Ontario. Her work focuses on unsteady separated flows, vortex-induced vibrations, and numerical simulation methodologies. Education: B.Sc. & M.Sc. (Middle East Technical University, 1979/1981), Ph.D. in Applied Mathematics (University of Western Ontario, 1987). Postdoctoral fellowships followed before joining Memorial in 1999 as Associate Professor, promoted to Full Professor in 2005. Research Interests: Interdisciplinary applied mathematics, theoretical fluid mechanics, computational science. Specializes in fluid-bluff body interactions, numerical methods for PDEs, and validation across computational/experimental studies. Key Contributions: Over 80 refereed papers, $2M+ in grants, and mentorship of >30 students/postdocs. Pioneered studies on free surface flows with moving bodies and developed parallelized CFD algorithms. First woman in Canada to win Petro-Canada Young Innovator Award (2000) CAIMS Arthur Beaumont Distinguished Service Award (2006) Teaching: Courses include Numerical Algorithms, Fluid Mechanics, Partial Differential Equations. Advocated for STEM education equity, training 17→50 graduate students in her department (1999–2009).

• Operations Research
• Healthcare Logistics
• Transportation Systems
• +4 more

Onur Ozturk is an Associate Professor at the Telfer School of Management, University of Ottawa. He holds a PhD in Industrial Engineering from Grenoble Institute of Technology (2012), followed by a postdoctoral fellowship at Ivey Business School (2012–2014). Prior to his current position, he worked as an Assistant Professor at ESIEE Paris (2014–2017). His research focuses on large-scale optimization in healthcare and transport sectors, leveraging scheduling theory, simulation modeling, and stochastic processes. He has contributed to projects such as the Efficacity Institute’s urban freight transport optimization in Île-de-France. Research interests include: Integer linear programming and operations research applications Scheduling and simulation modeling for healthcare logistics and transportation Optimization of pandemic-related operations (e.g., staff scheduling in hospitals, patient admissions) Bi-criteria optimization in medical sterilization and manufacturing systems Recent funded research includes a NSERC grant (2019–2026; $169,000) for batch scheduling methods, and multiple internal grants addressing healthcare routing (2021), medical sterilization (2021), and pandemic staffing (2020–2021). His work emphasizes practical solutions for urban logistics, healthcare capacity planning, and crisis management. No scientific awards are explicitly mentioned in the text. His teaching focuses on business analytics, operations management, and stochastic modeling.

Patricia EvansView profile

Professor

• Computational Biology
• Bioinformatics
• Algorithm Design and Analysis
• +5 more

Patricia Evans is a Professor and Associate Dean at the Faculty of Computer Science , University of New Brunswick. She holds a B.Sc. from the University of Alberta and M.Sc./Ph.D. from the University of Victoria. Research Focus : Computational Biology, Bioinformatics, Algorithm Design and Analysis, Graph Theory, Parameterized Complexity Current Projects : RNA structure comparison, haplotype inference, biological network analysis, parallelization of dynamic programming Collaborations : Eric Aubanel (UNB), Todd Wareham (Memorial University), Ken Kent (UNB), Jackie Rice (University of Lethbridge) Grants : NSERC, Genome Atlantic, AIF, NBIF Her research applies computer science theory to molecular biology problems, including RNA structure analysis and phylogenetic network modeling. Past work includes FPGA implementations for bioinformatics algorithms and parameterized complexity analysis for motif finding. Publications span 1999-2008, with key topics in RNA pseudoknot detection, haplotyping, and computational complexity. Her lab has mentored 13 graduate students, including 2 Ph.D. graduates.

Przemyslaw PochecView profile

Associate Professor

• Data Communication
• Image Processing
• Three-dimensional Computer Vision

Przemyslaw Pochec is an Associate Professor at the Faculty of Computer Science , University of New Brunswick, where he has held a faculty position for approximately 27 years. He holds a Master of Science in Computer Science and a Ph.D. from the University of New Brunswick. Research Interests: Data Communication, Image Processing, Three-dimensional Computer Vision His recent research focuses on queueing models for parallel computer systems , data communication system modeling, and stereo vision matching algorithms . While specific grants, awards, or student advisement records are not detailed in the provided text, his work bridges theoretical and applied aspects of computer science, particularly in communication systems and computer vision.

Ruppa ThulasiramView profile

Professor

• Computational Finance
• Cloud Computing
• Blockchain Technology
• +3 more

Ruppa Thulasiram is a Professor in the Department of Computer Science at the University of Manitoba, affiliated with the Faculty of Science. Her research and teaching focus on the intersection of computing and finance, with a particular emphasis on computational finance and cloud computing. University: University of Manitoba Department: Computer Science Academic Rank: Professor Email: Tulsi.Thulasiram@umanitoba.ca Office: E2-576 EITC Phone: 204-474-6538 Her research involves designing and analyzing parallel algorithms for finance problems like derivative pricing and portfolio optimization, leveraging cloud computing and blockchain technology for financial smart contracts. She also explores the multiscale mathematical and computational challenges inherent in financial systems. Ruppa leads the Computational Financial Derivatives and Cloud Computing Laboratory , focusing on robust computational methods and security analysis in blockchain-based financial applications. Her work bridges advanced mathematics, computer science, and finance.

• Phase-field modeling
• Computational materials science
• Solidification
• +5 more

Nikolas Provatas serves as an Adjunct Professor in the Department of Materials Science and Engineering within the Faculty of Engineering at McMaster University. His academic career spans over three decades with extensive contributions to computational materials science, particularly in phase-field modeling techniques. Provatas' research focuses on phase-field modeling, solidification phenomena, microstructure evolution, and computational materials engineering. His work bridges theoretical modeling with practical applications in additive manufacturing, metallurgy, and materials processing. He has developed advanced computational frameworks for simulating solidification processes, phase transformations, and microstructural development in various materials systems including metals, alloys, and nanomaterials. His research has significant implications for understanding fundamental materials phenomena and improving industrial manufacturing processes. Analysis of Provatas' extensive publication record reveals a strong trend toward increasingly sophisticated computational modeling approaches, particularly the phase-field crystal method. His work spans fundamental theoretical developments to practical applications in additive manufacturing, welding, and casting processes. The research shows particular emphasis on understanding microstructure evolution during solidification, phase transformations, and the relationship between processing conditions and final material properties. Provatas has mentored numerous students and collaborated extensively across the materials science community, though specific details of his advising record are not provided in the available information. His research has attracted significant attention, as evidenced by the substantial readership metrics across multiple platforms. His laboratory work centers on computational materials science, developing advanced simulation frameworks to model solidification phenomena, phase transformations, and microstructure evolution across multiple length and time scales. These computational approaches enable detailed investigation of materials behavior that would be challenging to observe experimentally.

Jean MahseredjianView profile

Professor

• Electrical Networks
• Electromagnetics
• Circuit Theory
• +4 more

Jean Mahseredjian is a Full Professor in the Department of Electrical Engineering at Polytechnique Montréal. He holds the NSERC/Hydro-Québec/RTE/EDF/OPAL-RT Industrial Research Chair in Multiscale Time Simulation of Transients in Large-Scale Electrical Networks and is a member of the Institute for Data Valorization (IVADO). His research bridges electrical engineering, numerical methods, and renewable energy integration. Research interests include: Advanced numerical simulation of power system transients Modeling and validation of wind farms and HVDC systems Electromagnetic compatibility and circuit theory Real-time simulation and parallel computing for large-scale grids His recent publications (2025-2026) demonstrate strong emphases on: EMTP simulation advancements for circuit breakers, cables, and converters Renewable integration challenges (wind/PV parks and battery systems) High-performance computing methods for power system analysis Awards and honors: IEEE Fellow (2013) for contributions to power system transients simulation Supervision and collaboration: Mentored 68 graduate students (31 PhD, 37 Master's) Leads industry-funded research with Hydro-Québec, RTE, EDF, and OPAL-RT Develops open-source simulation tools (e.g., Julia-based platforms)